Interrogating the topological robustness of gene regulatory circuits by randomization

Abstract

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

Role of noise and parametric variation in the dynamics of gene regulatory circuits

“…Here, the parametric variation (P) is defined as the spread of the parameter ranges relative to the parameter range used in the original RACIPE 20 while keeping the median constant. P is measured in percentages such that the ranges are same if P is set to 100, and a larger P implies a wider spread of parameter values.…”

“…Lastly, we applied sRACIPE to an EMT gene regulatory circuit, which we previously constructed from former studies and an extensive literature search 20 . Evidences [39][40][41][42] suggest that the EMT circuit controls the decision making of the cell transition from the epithelial to mesenchymal states during embryonic development, wound healing and cancer metastasis 40,41,43 .…”

“…We have recently developed a systems-biology modeling method, named random circuit perturbation (RACIPE) 20 , to deal with this long-lasting issue of parameter uncertainty. RACIPE takes the GRC topology as the only input, and generates an ensemble of models with random kinetic parameters.…”

“…From our previous tests on simple GRC motifs and the biological regulatory circuits governing epithelial-tomesenchymal transition (EMT) 20 and B-cell development 21 , etc., we found that the steady-state solutions from an ensemble of random models form several distinct clusters according to their expression patterns, which correspond to the functional states of the circuits (e.g. the functional states A ON B OFF and A OFF B ON for a toggle switch with two genes A and B).…”

“…We will illustrate the methods using the canonical double-well potential system. Afterwards, we will explain the integration of these stochastic analysis methods with RACIPE and apply the new method on simple toggle switches, coupled toggle switches and an epithelialmesenchymal transition (EMT) network 20,25 . We will demonstrate how the parametric variations and noise influence the functions of GRCs.…”